What is the difference between parametric and non-parametric models?
Parametric Methods
A parametric approach (Regression, Linear Support Vector Machines) has a fixed number of parameters and it makes a lot of assumptions about the data. This is because they are used for known data distributions. i.e, it makes a lot of presumptions about the data
Non-Parametric Methods
A non-parametric approach (k-Nearest Neighbours, Decision Trees) has a flexible number of parameters, there are no presumptions about the data distribution. The model tries to &quot;explore&quot; the distribution and thus has a flexible number of parameters.
Comparision
Comparatively speaking, parametric approaches are computationally faster and have more statistical power when compared to non-parametric methods

Hope this cleared your doubts